SEO is a wild world, and if you're using AI-generated content, you want a model that actually helps your pages rank instead of just spewing words. We took a bunch of popular LLMs and put them through the wringer to see which one is the most SEO-friendly straight out of the box—no fancy prompts, no tricks. Just raw AI power.
TL;DR - The Quick Hitters
Setup: Without further instructions, we asked the LLMs for a basic 1000-word article. Then, we asked them to generate an SEO-optimized 1000-word article—no other instructions were given.
Evaluation: We then analyzed the quality of the articles using PageOptimizer Pro (POP) to measure their effectiveness in meeting SEO best practices.
🏆 Best Overall SEO Performer: Qwen 2.5-Max (Alibaba) - consistently the highest POP scores (though not that great), good structure, and solid content length.
📉 Worst Overall: Gemma 2 (Google) - bottom of the barrel in almost every category. If you're using it for SEO without a lot of prompts and editing, good luck.
💡 Biggest Glow-Up: Llama 3.1 (Meta AI) - went from one of the worst in the baseline test to one of the biggest improvers when given SEO instructions.
📢 Where They ALL Struggled - main content, sub-headings, and Google NLP alignment - The shocking part? Word counts and important term counts actually went down in the SEO-optimized versions instead of increasing. These models should have been nailing the language and expanding the content, but instead, they shrunk it! That's a huge red flag because SEO thrives on depth, and AI should be capable of generating more, not less, when given structured SEO guidance.
Instead of improving keyword density and depth, most models just formatted better headings—but failed to produce enough content to meet SEO needs. No models hit the ideal important terms range, and Google NLP scores remained unimpressive.
If you think LLMs should excel at language generation, think again—this is where they struggle the most. Formatting? No problem. But actually creating in-depth, SEO-friendly content? That’s where they fall apart.
✅ Best of the Worst for SEO-Optimized Content:Qwen 2.5-Max (Alibaba), GPT-4o, and Claude 3.5 Sonnet - If you're aiming for mediocre results, these models might get you to page 5 on Google. They follow structure better than the rest but still miss the mark on actual content depth and keyword strategy. If you're serious about ranking, you'll want something more powerful—like PageOptimizer Pro (POP) and White Glove—to get real SEO wins.
Evaluation Criteria (How We Judged SEO Performance)
To see which AI models actually help with SEO, we looked at these key factors:
- POP Score – Think of this like a report card for how well the AI follows SEO best practices. Higher scores mean better content for ranking on Google. Most web pages start to get good movement in Google when they hit an 80% score in POP.
- Word Count – Longer content can be better for SEO, but only if it stays relevant and useful. We checked if the AI wrote enough to cover topics properly, or could even just follow the instruction that we wanted 1000 words.
- Readability Scores (Can humans actually read this?)
- Reading Ease Score – The higher the score, the easier it is to read. Simple and clear wins for SEO!
- Reading Level – Labels content as 'High School,' 'College,' etc., to see if it's too complex for general readers. The general guideline for webpages is to have your content at a 7th grade level for easy reading and comprehension.
- Does the AI understand SEO structure?
- Title Tag (2-5 important words) [LSI & Variations] – Did the AI create an effective title that helps Google understand the topic?
- Headline (2-5 important words) [LSI & Variations] – Did the AI write a headline relevant to the topic?
- Subheadings (10-18 total import words) [LSI & Variations] – Good articles need clear sections. Did the AI break the content into useful parts and include important words in the subheadings for each section?
- Content Length (167-278 total important words) [LSI & Variations] – Did the AI write enough in each section, or was it too short and did it include the right amount of important words?
- Google NLP (14-60 total important words) [Google NLPs in main content area] – This refers to the number of semantic words related to the topic that should be included to help Google understand the content. Hitting the right range (14-60 words) ensures that the AI is including enough related terms for strong search relevance.
How We Tested
We ran two tests on each model:
- Baseline Prompt (No SEO Instructions): Just asked for a 1000-word article, no fancy SEO tweaks:
"Write an article about what to see in Rome in 1000 words. Output html format."
- SEO-Optimized Prompt: Gave basic SEO instructions to see how well they followed.
"Write an SEO-optimized article about what to see in Rome in 1000 words. Output html format."
We measured POP Score (SEO effectiveness), Word Count, Readability, and LSI/Variation and Google NLP Structure (headings, content breakdown, and NLP alignment).
LLM Comparison: Baseline Prompt (No SEO Instructions) - Raw AI Output
1. POP Score Analysis
POP Rank Engine™️ is the brain powering the on-page SEO tools inside POP. Developed over more than half a decade through more than 400 SEO experiments led by Kyle Roof, POP Rank Engine™️ is an ever-evolving machine that analyzes over 300 parameters to deliver the most comprehensive and powerful recommendations. Its single mission? Making it as easy and fast as possible for you to dominate search rankings and grow SEO traffic.
A high POP Score means you’re creating content Google actually wants to rank. A low score? Good luck getting past page five.
The POP Score is the key measure of how well AI-generated content aligns with SEO best practices. Most pages start to get good movement in Google when they hit a POP score of 80%. Here’s how different models performed with the baseline prompt:
🏅 Best & Worst in POP Score:
✅ Best: Qwen 2.5-Max (64.04) - It gets SEO structure right.
GPT-4o (OpenAI): 57.4 - Reasonable SEO alignment.
Claude 3.5 Sonnet (Anthropic): 54.45 - Decent, but not great.
❌ Worst: Gemma 2 (12.1) - Basically the AI equivalent of a brick for SEO.
2. Word Count Comparison
It’s annoying that many LLMs won’t give you the word count you ask for. Word count in SEO is an important part of putting out good content that can rank. Here’s how the models performed.
✍️ Who Nailed the Word Count?
🎯 On Target: Claude 3.5 Sonnet (1,023 words) - nailed the 1000-word request.
📢 Too Chatty: GPT-3.5 (1,394 words) - clearly doesn't know when to stop talking.
📉 Way Too Short: Gemma 2 (537 words) - SEO content needs meat, and this one was barely a snack.
3. Readability Metrics
Readability isn’t a ranking factor but most people want internet content to be around a 7th grade level so that their visitors can read and understand the content quickly.
📖 Readability Winners & Losers:
🏆 Easiest to Read: GPT-4o (55.09 Reading Ease Score) - If you want content that’s smooth and digestible, this is your model.
📚 Most Complex: Claude 3.5 Sonnet (35.06 Reading Ease Score) - Get ready to decode some dense AI text.
4. Variations & LSI Terms Analysis
Latent Semantic Indexing (LSI) terms and keyword variations are crucial for ranking because Google expects content to include related and supporting terms that enhance topic relevance. A well-optimized article should spread these terms across subheadings and main content to maximize ranking potential.
📢 Search Engine & Page Titles (Target: 2-5 important words)
Most models generated optimized titles with 2 important terms, which is within an acceptable range. However, Gemini 2.0, Llama 3.1, and Gemma 2 only generated titles with 1 important term, meaning they missed a critical SEO signal—properly structured titles help Google understand the page topic better and increase click-through rates. Missing an optimized title means your page may not clearly communicate relevance to Google, hurting ranking potential.
📌 Sub-headings (Target: 10-18 LSI/Variations important words in Sub-headings)
All models failed to reach the ideal range, making their structure weak. Headings aren’t just for readability—they help Google break down content for better indexing and ranking. Qwen 2.5-Max (8) and DeepSeek R1 and Perplexity AI (6) performed the best, but even they didn’t hit the ideal range of 10-18 important words in the headings. Without enough important words in the subheadings, the content lacks clarity and scannability, making it harder for both users and search engines to understand the key sections of the page.
📉 Main Content (Target: 167-278 LSI/Variations words in Sub-headings)
Only GPT-o3-mini (53) and Qwen 2.5-Max (52) approached the ideal content depth per section. A well-optimized page needs enough text in each section to properly develop topics with supporting LSI terms, and improve ranking. Gemma 2 (14) performed the worst, meaning it lacked sufficient content in key areas, making it highly unlikely to rank well. If a section doesn’t provide enough supporting details, it fails to reinforce keyword relevance, weakening the page’s authority in Google's eyes. This is one of the most critical ranking factors—thin content won’t cut it. If a section doesn’t provide enough supporting details, it fails to reinforce keyword relevance, reducing the page’s chances of being seen as authoritative by search engines.
5. Google NLP Terms Analysis
Google NLP scores measure how well an article aligns with Google’s understanding of a topic, ensuring that enough critical keywords and entities are present for strong ranking signals.
🔝 Best Google NLP Score (Target: 14-60 relevant words): Qwen 2.5-Max included 22 critical keywords, and GPT-4o included 20, meaning their content closely matched what Google expects for strong topic relevance and ranking potential. - These models included the most essential terms Google expects, giving them a ranking advantage.
❌ Worst Google NLP Score (Target: 14-60 relevant words): Gemma 2 & Gemini 2.0 only included 7 critical keywords, making it less relevant in Google’s eyes and likely to struggle in rankings. - Google barely recognizes what it’s talking about, which is a major problem for search visibility.
LLM Comparison: SEO-Optimized Prompt - Do These Models Actually Understand SEO?
1. POP Score Analysis
For this test, we kept it simple—we just told the LLMs to give us an 'SEO-optimized' article. No deep guidance, no keyword lists, no extra instructions—just, 'Make it SEO-friendly.' The goal? To see if these models actually understand what SEO content means on their own.
Turns out, they don’t.
Instead of delivering well-optimized, keyword-rich content, most of these models reshuffled words, added fluff, and barely touched keyword strategy. While some scores improved, it was clear they were just formatting text differently, not optimizing for search rankings.
🏅 Best & Worst in POP Score:
✅ Best: Qwen 2.5-Max (64.04) - Still the leader.
📈 Biggest Gain: Llama 3.1 (+42.79) - Major improvement.
📉 Only One That Got Worse: Qwen 2.5-Max (-1.75) - Oddly declined slightly. While still the best, this drop suggests it may not be fully optimizing beyond its already strong baseline.
2. Word Count Comparison
✍️ Who Nailed the Word Count?
✍️ Best Word Count Accuracy: GPT-o3-mini (1063 words) - Near perfect for SEO length.
📢 Too Long: Llama 3.1 (1181 words) - Slightly exceeding the ideal range.
📉 Worst Performers (Too Short for SEO): Gemini 2.0 (551 words), GPT-4o (553 words), Perplexity AI (567 words), DeepSeek R1 (573 words) - These models failed to meet the ideal content length, making them less competitive for SEO. Without enough content, important keywords and supporting details are missing, reducing their ability to rank effectively.
3. Readability Metrics
📖 Readability Winners & Losers:
🏆 Best Readability: GPT-4o (9.81 Grade Level, 50.01 Reading Ease) - Most accessible model.
📚 Still Hardest to Read: Gemma 2 (29.86 Reading Ease) - Borderline unreadable. Complex language doesn’t mean quality, and in SEO, clarity wins.
4. LSI and Variations Analysis
When given basic SEO instructions, the models showed slight improvements in structure—but still fell short in content depth and keyword strategy. Here’s how they performed when asked to optimize for SEO:
📢 Search Engine & Page Titles (Target: 2-5 words):
✅ No Major Changes: Most models didn’t improve their titles even when prompted for SEO optimization.
📌 Bare Minimum Adjustments:
- Most models stuck to two LSI/Variation words in titles—no extra keyword variation or strategic enhancements.
- Gemini 2.0 & Gemma 2 fell short, generating only one LSI/Variation word in title instead of the ideal two. This is a critical SEO failure since optimized titles are essential for better search relevance and click-through rates.
📌 Sub-headings (Target: 10-18 LSI/Variations words in Sub-headings)
📢 Some Gains, But Still Weak Overall
- DeepSeek R1 led with 10 important terms in subheadings—the only model to hit the lower end of the SEO-optimized range.
- GPT-4o improved to 9 important terms, showing slight structural progress.
- Qwen 2.5-Max (8), Claude 3.5 Sonnet (8), and Perplexity AI (7) followed behind, but none reached the ideal 10-18 range.
❌ Biggest Failure: Gemma 2 (0 LSI/variations words subheadings)—essentially no important terms in structure, meaning Google will struggle to index its content properly.
📉 Main Content (Target: 167-278 LSI/Variations words in Sub-headings)
📊 Did the AI Models Expand Content Depth?
🚨 Nope. In fact, most failed to deliver adequate depth per section.
Best Performers: Qwen 2.5-Max (50), Claude 3.5 Sonnet (42), and GPT-o3-mini (46) had the most content per section.
Still Too Thin: GPT-4o (28), Perplexity AI (27), and Llama 3.1 (29) remained below optimal depth.
❌ Worst Case: Gemma 2 (10 LSI/Variation words in main content)—a massive SEO failure, making it nearly impossible to rank well.
5. Google NLP Terms Analysis
✅ Best Performer: GPT-o3-mini (19 NLP terms) – This model included the most relevant keywords, making it the most aligned with Google’s expected topic structure.
📉 Declining NLP Scores: Qwen 2.5-Max dropped from 22 to 16, meaning it actually lost important topic-related terms instead of improving.
🚨 The Worst Performers:
- ❌ Gemma 2 (1 NLP term) – Completely failing to include key ranking terms, making it nearly invisible to Google’s algorithms.
- ❌ Gemini 2.0 (6 NLP terms) – Still far below the recommended range, meaning Google may struggle to categorize its content properly.
POP Score Analysis: Baseline vs SEO-Optimized Performance
At first glance, it looks like many AI models improved their POP Scores after being prompted for SEO. But when we dig deeper, we see that these gains came almost entirely from better structuring—not from actual SEO substance.
🚀 The “Improvements” That Aren’t Really Improvements
📈 Llama 3.1 (Meta AI) saw the most dramatic increase (+42.79), suggesting it responded well to structuring prompts.
📈 Perplexity AI, GPT-o3-mini (OpenAI), and Gemma 2 (Google) also had major score jumps, meaning they formatted their content better—but did they actually improve SEO depth? No.
📈 Claude 3.5 Sonnet showed steady growth (+5.39), making it more structured but still lacking in essential SEO elements.
📉 GPT-4o (OpenAI) barely moved (+1.57), which suggests its natural writing style aligns with SEO, but it still failed to improve keyword strategy.
📉 Qwen 2.5-Max (Alibaba) was the only model that declined (-1.75), but that’s likely because its baseline was already well-optimized compared to the others.
THE Big Reveal: AI Models are Stripping out Critical SEO Content
We expected AI models to enhance content when prompted for SEO, expanding word count and improving Latent Semantic Indexing (LSI) and keyword variations.
📉 Instead, they did the opposite—producing LESS content, using FEWER relevant words, and completely failing to optimize for SEO.
The Hard Numbers: AI Is Stripping LSI & Variations from Content
When analyzing how well AI-generated content incorporated LSI terms and keyword variations, we found shocking results:
- GPT-o3-mini lost 7 LSI/Variation words per section.
- Claude 3.5 Sonnet (-2) and Qwen 2.5-Max (-2) both regressed.
- GPT-4o tanked by losing 8 key terms.
- Gemini 2.0 completely collapsed, shedding 12 words.
- Perplexity AI failed even harder, losing 9 words per section.
- Gemma 2, already the worst, somehow got even worse.
💡 Most models either failed to increase keyword variations or actively removed them—making their SEO performance worse.
The Ugly Truth: AI is Prioritizing Formatting Over SEO Substance
📌 Yes, some models saw a bump in SEO scores—but ONLY because they structured their headings better.
📌 The actual content—the part that matters for SEO—became significantly worse.
📌 Google doesn’t rank content based on how pretty your headings look—it ranks content based on its keyword depth and relevance.
Instead of strengthening content for search engines, AI is:
❌ Stripping LSI terms and variations that drive rankings.
❌ Ignoring Google’s expectations for keyword depth.
❌ Leaving content weaker, less authoritative, and ultimately harder to rank.
AI Is Gaming SEO—POP Just Proved It
At first glance, it might seem like POP scores increasing while main content depth and LSI terms are decreasing is a contradiction. But actually, this exposes a fundamental flaw in AI-generated SEO content.
Here’s why:
1️⃣ POP Measures Structure and SEO Best Practices—But AI is Gaming the System
POP scores reflect how well content follows SEO best practices—things like:
✅ Proper use of headings and subheadings
✅ Title tags and keyword placement
✅ Structural organization
✅ Readability and formatting
AI models figured out how to “win” in these categories without actually improving the substance of their content.
🚨 In other words, AI models are optimizing for the easiest SEO wins (headings, formatting) while actively making the most important part—main content—WORSE. 🚨
2️⃣ POP Exposes the AI Problem
If anything, this test proves that AI models CANNOT be trusted for SEO.
- AI improves headings and structure because that’s easy.
- AI butchers main content depth and LSI terms because it doesn’t understand SEO substance.
- POP caught this failure.
👉 Without POP, you wouldn’t even know AI was wrecking your content!
🚨 Final Verdict: AI is Gaming SEO, and POP Exposed it
❌ AI models tricked their way to better scores by only fixing the surface.
❌ If AI-generated content actually improved SEO, we’d see LSI terms and content depth INCREASING—not decreasing.
✅ POP exposed the problem— the critical components of SEO that POP looks at are the reason we caught this in the first place.
💡 The solution? AI-generated content can’t rank on its own. You need expert human optimization to make sure your content isn’t just structured well, but actually SEO-effective.
Co-Founder & Lead SEO at POP
Kyle Roof is an SEO expert, speaker and trainer. Kyle currently resides in Chiang Mai, Thailand with his family.
